Markov state model

Work units are a fraction of the simulation between the states in a Markov state model.

However, Anton does not use Markov state models for analysis.

Adaptive sampling is used by the Folding@home distributed computing project in combination with Markov state models.

Using the Markov state model approach, Folding@home achieves strong scaling across its user base and gains a linear speedup for every additional processor.

The conformational states from Rosetta's software can be used to initialize a Markov state model as starting points for Folding@home simulations.

In 2006, scientists applied Markov state models and the Folding@home network to discover two pathways for fusion and gain other mechanistic insights.

As the simulations discover more conformations, the trajectories are restarted from them, and a Markov state model (MSM) is gradually created from this cyclic process.

The amount of time it takes to construct a Markov state model is inversely proportional to the number of parallel simulations run, i.e. the number of processors available.

In 2002, Folding@home used Markov state models to complete approximately a million CPU days of simulations over the span of several months, and in 2011, MSMs parallelized another simulation that required an aggregate 10 million CPU hours of computing.

The adaptive sampling Markov state model approach significantly increases the efficiency of simulation as it avoids computation inside the local energy minimum itself, and is amenable to distributed computing (including on GPUGRID) as it allows for the statistical aggregation of short, independent simulation trajectories.